Blog Post:Here are a couple of common questions I get from digital marketers: "Are my paid search keywords cannibalizing my natural search keywords?" And "How much should I really be spending on my paid search bids?" Believe it or not, using a program called "R", you can actually figure this out using some statistical number crunching and Report Builder. Here's how to do it:
First, you need to first download how much you spent on your paid keywords each day over a long time period (preferably a year or more). The data should be readily available through a digital ad management platform like Adobe Media Optimizer.
The dataset we need should look something like this:
Notice that the total spend is broken down by each day, and I've even split out branded vs. non-branded.
Next, using Adobe ReportBuilder, download the total number of visits, visitors, conversions, and any other potentially relevant metrics, each subtotaled by day, and add it to the dataset we just created. This will give us a master dataset that we can start modeling off of.
Here's where the fun starts. If you're not familiar with R, I'd highly recommend becoming so. It's a useful and powerful open source statistical engine. A detailed explanation of R is outside the scope of this article, but Adobe Consulting is always ready to help if you need it.
So, let's get started. First we need to load the data into R. You can save what you've downloaded from ReportBuilder as a CSV file and easily import that file into R using the read.csv function. We'll now use R to create a multiple regression model (borrowed from some common econometric principles) that will estimate how much your paid search is cannibalizing your natural search:
The trick here is to include the total visits and other traffic related variables in your multiple regression to account for the ebbs and flows of traffic and seasonality. You can implement a regression model with the R function "lm". You'll want to set natural searches as the dependent variable and set visits, paid searches, and other traffic related variables as your independent variables.
Once R spits out the results, you'll want to take a look at the estimated coefficient for paid searches. The coefficient represents the model's estimate of how your paid searches are affecting natural search. If the coefficient is negative, that means that paid search is pulling away from your natural search to some extent. The actual value of the coefficient will give you an idea of just how much this is happening. I've seen this value range anywhere from -0.3 to -0.7, meaning cannibalization between 30% and 70%.
To find your optimal spend amount in paid search, we're going to use a marketing model called "ADBUDG". This model will create estimates of zero paid search spend return and paid search spend saturation (read: how much return will I get if I spend nothing on paid search, and how much can I expect if money was no object).
Fitting this model is a little complex, so I won't get into the gritty details here, but the output will look something like this:

The fitted line represents how many conversions you might expect (on average) at a given paid search spend level. It also gives you an idea of how fast you'll reach your saturation point.
Finally, with this curve, you can now estimate how much paid search is really worth to you. If you have an idea how much your site's conversion event is worth in dollars, you can create a profit curve using the model above:

These curves show you what your break-even points are for different conversion profit margins. You can see that if my conversion is worth $150, I'd be willing to spend more on paid search compared to if my conversion was only worth $50.
So what have we learned here?

SiteCatalyst data can be used to determine natural search cannibalization

You can also figure out how much conversion you might expect without spending anything on paid search, and conversely, the most you could ever expect.

If your conversion is worth a dollar amount, you can calculate exactly how much paid search is worth to you.

Is Paid Search Cannibalizing Your Natural Search?

Here are a couple of common questions I get from digital marketers: “Are my paid search keywords cannibalizing my natural search keywords?” And “How much should I really be spending on my paid search bids?” Believe it or not, using a program called “R”, you can actually figure this out using some statistical number crunching and Report Builder. Here’s how to do it:

First, you need to first download how much you spent on your paid keywords each day over a long time period (preferably a year or more). The data should be readily available through a digital ad management platform like Adobe Media Optimizer.

The dataset we need should look something like this:

Notice that the total spend is broken down by each day, and I’ve even split out branded vs. non-branded.

Next, using Adobe ReportBuilder, download the total number of visits, visitors, conversions, and any other potentially relevant metrics, each subtotaled by day, and add it to the dataset we just created. This will give us a master dataset that we can start modeling off of.

Here’s where the fun starts. If you’re not familiar with R, I’d highly recommend becoming so. It’s a useful and powerful open source statistical engine. A detailed explanation of R is outside the scope of this article, but Adobe Consulting is always ready to help if you need it.

So, let’s get started. First we need to load the data into R. You can save what you’ve downloaded from ReportBuilder as a CSV file and easily import that file into R using the read.csv function. We’ll now use R to create a multiple regression model (borrowed from some common econometric principles) that will estimate how much your paid search is cannibalizing your natural search:

The trick here is to include the total visits and other traffic related variables in your multiple regression to account for the ebbs and flows of traffic and seasonality. You can implement a regression model with the R function “lm”. You’ll want to set natural searches as the dependent variable and set visits, paid searches, and other traffic related variables as your independent variables.

Once R spits out the results, you’ll want to take a look at the estimated coefficient for paid searches. The coefficient represents the model’s estimate of how your paid searches are affecting natural search. If the coefficient is negative, that means that paid search is pulling away from your natural search to some extent. The actual value of the coefficient will give you an idea of just how much this is happening. I’ve seen this value range anywhere from -0.3 to -0.7, meaning cannibalization between 30% and 70%.

To find your optimal spend amount in paid search, we’re going to use a marketing model called “ADBUDG”. This model will create estimates of zero paid search spend return and paid search spend saturation (read: how much return will I get if I spend nothing on paid search, and how much can I expect if money was no object).

Fitting this model is a little complex, so I won’t get into the gritty details here, but the output will look something like this:

The fitted line represents how many conversions you might expect (on average) at a given paid search spend level. It also gives you an idea of how fast you’ll reach your saturation point.

Finally, with this curve, you can now estimate how much paid search is really worth to you. If you have an idea how much your site’s conversion event is worth in dollars, you can create a profit curve using the model above:

These curves show you what your break-even points are for different conversion profit margins. You can see that if my conversion is worth $150, I’d be willing to spend more on paid search compared to if my conversion was only worth $50.

So what have we learned here?

SiteCatalyst data can be used to determine natural search cannibalization

You can also figure out how much conversion you might expect without spending anything on paid search, and conversely, the most you could ever expect.

If your conversion is worth a dollar amount, you can calculate exactly how much paid search is worth to you.

If this analysis seems too tough to tackle on your own, enlist the help of Adobe Consulting who can guide you through the process and have you back on track to paid search profitability.

Trevor Paulsen

Trevor Paulsen works as a product manager specializing in the use of data mining and predictive analytics techniques for customer intelligence for the Adobe Analytics compute platform. He has designed and implemented predictive analytics solutions for many Fortune 500 organizations. Trevor’s focus involves looking beyond descriptive web analytics to build models that can help organizations predict and understand trends in their customers' behaviors. Previously Trevor worked at Boeing's Insitu division creating autonomous aircraft for the Navy and as the lead of Adobe's data science consulting team. Trevor has an MBA and an MS in computer engineering from BYU. He lives in Lehi, Utah with his wife and three children.